Predictive maintenance has become the new grail for railway rolling stock manufacturers. The aim is to produce a maintenance plan adapted to the real needs of the rolling stock. The aim is to avoid getting trains in the workshop that do not need it or, conversely, to anticipate an unexpected entry into the workshop following the detection of a possible imminent breakdown. To achieve this, trains must become IoTs, the Internet of Things.
Until now, the maintenance of rolling stock has been essentially preventive: after X thousands of kilometres, a train must return to the workshop to check the wear of the axles. But are all axles worn out after an arbitrary number of kilometres? The answer is often yes, based on past experience and a number of analyses.
In the early 1970s, at the SNCF in France, the maintenance policy gave priority to the ergonomics of people at work by providing as much as possible for daytime and weekday work, even if it required more rolling stock. This worker-centred policy came to an end in the 1990s. Every downtime is a cost factor, since during its time in the workshop a train no longer enters the commercial system, as the seats are not sold.
Of course, the trick is to replace the stopped train with another train fit for service. But the multiplication of this solution means that for a given service you need, for example, 20 trains, knowing that 3 are in the workshop. But with predictive maintenance, you could imagine that you would only need 16 trainsets, with one or two likely to be in the workshop as a reserve in case of a sudden incident.
To avoid buying 4 extra trainsets is obviously a step forward for small operators, but it means that you must to have a very sophisticated maintenance programme to ensure maximum availability, which varies over time. Manufacturers have therefore understood the importance of making their railway products more ‘digital’. But not only for the operational service of the operators.